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2015 | OriginalPaper | Chapter

Tactile Object Recognition with Semi-Supervised Learning

Authors : Shan Luo, Xiaozhou Liu, Kaspar Althoefer, Hongbin Liu

Published in: Intelligent Robotics and Applications

Publisher: Springer International Publishing

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This paper introduced a novel approach to recognize objects with tactile images by utilizing semi-supervised learning approaches. In tactile object recognition, the data are normally insufficient to build robust training models. Thus the model of Ensemble Manifold Regularization, which combines concepts of multi-view learning and semi-supervised learning, is adapted in tactile sensing to achieve better recognition accuracy. Different outputs of classic bag of words with different dictionary sizes are considered as different views to produce an optimized one based on multiple graphs learning optimization. In the experiments 12 objects were used to compare the classification performances of our proposed approach and the classic BoW model and it is proved that our proposed method outperforms the classic BoW framework and objects with similar features can be better classified.

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Metadata
Title
Tactile Object Recognition with Semi-Supervised Learning
Authors
Shan Luo
Xiaozhou Liu
Kaspar Althoefer
Hongbin Liu
Copyright Year
2015
DOI
https://doi.org/10.1007/978-3-319-22876-1_2

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